GEOG 438: Advanced Remote Sensing

Background The goal of this lab is to learn and utilize eCognition, a state of the art object-based processing tool. First  homogeneous spatial and spectral clusters are produced over the image or known as objects. a random forest and support vector machine classifiers will be used which require training samples. Finally an output will be produced showing the newly classified image. Methods First a project was created in eCognition, then objects need to be created on the image, this is done through the process tree utilizing multiresolution segmentation, with a scale parameter of 9. This produced the objects shown in figure 1 and 2. Finally training samples are taken to be put in the process tree. this trains the classifier. Figure 1: This shows a mess of blue over the image, but thwese are all polygons that contain similar pixels. Figure 2: This image shows a zoomed in section of figure 1, this allows the actual polygons to be seen. Next the same process was performed...

Background The goal of this lab is to develop an image that shows change detection of land surface features over time. First a means of performing a qualitative change detection will be performed, then we will quantify post-classification change detection, and a model will be developed that will map detailed change of land use land cover images over time. Methods Part 1: Change detection using Write Function Memory Insertion The first part of the lab, a Write Function Memory Insertion is utilized. This processes utilizes near infrared bands, when the two images are brought into the Write Function, an output image is produced, and the image has areas highlighted in pinkish red, these are areas that have experienced change. Specifically in this lab, ec_envs1991.img and ec_envs_2011.img were used to perform this change detection. Part 2: Post-classification comparison change detection   In this part of the lab a From-to change detection using classified images of the Milw...

Goal The goal of this lab is to understand how to evaluate the accuracy of classification results. Accuracy assessments are mandatory after performing an image classification, this is a portion of the post-processing stage of remotely sensed data. Methods An accuracy assessment is performed on a supervised and an unsupervised classification from labs 3 and 4. This is done by utilizing the accuracy assessment tool in Erdas. A high resolution reference image of the same study area is used to perform the assessment. This is done by generating  random points on the reference image, 125 random points in this lab,  and classifying those points based on the high resolution image utilizing the same classification codes for water, forest, agriculture, etc. (Table 1). By identifying these points, a comparison report is then produced (Figure 1). This allows for an error matrix to be created from the results of comparing the random points to the classified images. Table 1: ...

Pixel-based Supervised Classification Goal The goal of this lab is to continue in learning the art of extracting biophysical and sociocultural information from remotely sensed images through pixel-based supervised classification. This is one of the most important skills in remote sensing. In the first portion of the lab, skills will be developed in selecting training samples to train a supervised classifier. Next the quality of the signatures will be evaluated. Then meaningful Land use/land cover classes will be produced in the final output. A Landsat 7 (ETM+) image captured on June 9, 2000 will be utilized with an Area of interest covering Eau Claire and Chippewa counties this image is ec_cpw2000.img.   Methods Part 1: Collection of training samples for supervised classification In this first part of the lab, training samples (spectral signatures) are collected to train a maximum likelihood classifier which will be used to classify the ec_cpw2000.img image. This...

Goal The main goal of this lab is to understand the art of extracting biophysical and sociocultural information from remotely sensed images, utilizing unsupervised classification algorithm. First in the lab we will become familiar with understanding the input configuration requirements and execution of an unsupervised classifier. Then the art of recoding multiple spectral clusters generated from unsupervised classifiers into thematic informational land use and land cover classes that meet a classification scheme. Methods Part 1: Experimenting with unsupervised ISODATA classification algorithm   Section 1: Setting up an unsupervised classification algorithm     In part one of the lab, we will  be experimenting with unsupervised ISODATA classification algorithm. First off, an Eau claire and Chippewa Image was formated to an unsupervised classification, utilizing an ISODATA algorithm. Figure 1: Classification scheme utilized when performing the ISODATA ...

Goal The main goal of this lab is to gain practical experience on correcting remotely sensed images for atmospheric correction. This lab will provide the knowledge to perform multiple methods for absolute atmospheric correction, and additionally relative atmospheric corrections will be performed. The two absolute atmospheric correcting methods that were used are Empirical Line Calibration (ELC) and Dark Object Subtraction (DOS). Additionally the Relative Atmospheric correction process requires using a multidate image normalization process. Methods Part 1: Absolute atmospheric correcting using empirical line calibration  In the first part of this lab atmospheric correction was performed on an Eau_Claire2011.img. This is a Landsat 5 TM image, that was collected on August 3rd 2011 at 10:41 am CST. In order to remove the atmospheric interference from this imaghe, spectral libraries are necessary for providing in situ data to help understand spectral profiles of surfaace features...

Goal and Background The goal of this lab is to understand and utilize the skills for extracting land surface temperatures from thermal bands from satellite images, and additionally account for these variations in land surface temperature over different areas. This lab will allow us to visually identify variations in relative land surface temperature. Model building will be used to quantitatively estimate surface temperatures from thermal bands, then more complex models will be utilized to accomplish the goal in one model rather than using multiple models or steps. Methods Part 1: Visual identification of relative variations in land surface temperature To begin the first part of this lab,  Landsat ETM+ images from band 61 and 62 are utilized. In this portion of the lab the tonal quality of each image is analyzed to determine temperature differences. Then the spectral range, and spatial resolution were determined.this portion of the lab helped in the process of understanding the...